There are many ways of determining the position of an unknown radio transmission transmitter/source. Assuming that the radio transmission source is stationary, then methods well known in the art such as radio triangulation and multilateration can be used to determine the position of a radio transmission source.
Typically, a radio signal receiver, or a cooperating group of radio signal receivers can be positioned at different positions relative to an unknown radio signal transmitter and pick up radio signal data from it. The different sets of data can then be correlated with one another to infer the position of the unknown radio signal source. Radio signal data from transmitters such as cellular telephone base stations, television, public radio transmitters and the like comprise distinguishing radio signal characteristics—such as code words—that can be used in such data correlation.
The collected and correlated radio signal data can then be used to provide feedback to a user about the location of the radio signal transmitter. For example, this may be achieved via an electronic display showing an icon overlaid onto a map, the icon representing the position of the radio signal transmitter/source.
Whilst, in theory, only a single set of radio signal data is needed to derive a single solution for the position of the radio signal source, in practice many sets of data are required to determine the position of the radio signal source to a degree of certainty—for example, to compensate for multi-path signal errors.
After collecting many sets of data, a so-called “error ellipse” can be drawn which shows the precision of the position estimate.
For example, with reference to FIG. 11, after taking six readings, six estimates A, B, C, D, E, and F of the position of a radio transmission source are obtained. As these reading are not the same as one another, a first error ellipse ee1 is defined, representing the error on the position of the radio transmission source.
During the course of taking a number of readings, a spurious reading may be received. For example, this is represented in FIG. 11 as position estimate G which returns a position significantly spaced from the six previous position estimates A, B, C, D, E, and F.
One way of dealing with such a reading G is to simply incorporate it into the existing set of readings—and increase the size of the error ellipse—thereby creating a second bigger error ellipse ee2. However, doing this may be inappropriate and could incorrectly increase the error ellipse and/or incorrectly skew the position estimate average. Therefore, when calculating the position of a radio signal transmitter, such erroneous readings tend to be ignored so as to collapse the error ellipse and get a more precise determination of the position of the radio signal transmitter.
However, it is not necessarily correct to ignore a reading that results in a significantly different location of a radio signal transmitter from a previous set. It may be appropriate, in some cases, to take it into consideration especially if that radio signal transmitter is mobile. How such a reading is treated depends on the way the radio signal data is correlated.
Therefore, one problem is determining how best to localise the position of a radio signal source of an unknown type—in particular for which the mobility is unknown. If a radio signal source is assumed to be stationary, but in fact is mobile, then readings of the position of the radio signal source may be incorrectly discarded as erroneous until a sufficient number of readings have been received to feedback a correctly updated position to a user. This could lead to a lag between the radio signal source having actually moved, and the same being represented by the navigation system. If the radio signal source is assumed to be mobile, but in fact it is stationary, this generally leads to the represented position of the radio signal source erroneously fluctuating making it difficult to pinpoint, and also incorrectly returning a larger error ellipse than appropriate.
Radio source mobility is not the only factor affecting localisation. For example, the quality of a radio transmission source can also be influential in localising that source. Clock drift on transmitters can vary significantly. Transmitters with stable oscillators (e.g. cellular transmitters) produce more reliable radio signal sources from which to localise—and so will tend to have a smaller error ellipse—than those with relatively unstable oscillators (e.g. low-grade walkie-talkies). Therefore, the amount of time needed to localise a low quality radio transmission source will be greater, and the error ellipse will be larger than for that of a high quality radio transmission source. However, if the quality of the radio source is not known, then a localisation system may expend too many resources, or take a disproportionate amount of time in processing the radio signal data. For example, it may take too long to be assured of the position of a high quality radio source and it may also take too long in attempting to resolve the exact position of a low quality radio source (when in fact only a rough estimate is possible).
It is an object of the present invention to alleviate the above-mentioned problems.